Image blur detection methods and arrangements

ABSTRACT

An apparatus is provided for detecting blur in an image. The apparatus includes an image generator that is configured to generate a plurality of corresponding different resolution images based on a base image. The plurality of corresponding different resolution images is provided to an edge detector. The edge detector detects edge transitions in each of the plurality of corresponding different resolution images and provides edge maps to an edge parameter comparator. The edge parameter comparator compares corresponding edge parameters as detected by the edge detector and provides a result map to a blur calculator. The blur calculator determines at least one blur parameter based on the result map and provides the blur parameter to a blur detector. The blur detector then determines if the base image is blurred based on a comparison of the blur parameter with at least one blur parameter threshold.

TECHNICAL FIELD

The present invention relates generally to computer imaging, and moreparticularly to improved image blur detection methods and arrangementsbased on edge detection and follow-on comparison calculations.

BACKGROUND

With the increasing popularity of personal computers, handheldappliances and the like, there has been a corresponding increase in thepopularity and affordability of image rendering/manipulationapplications.

Thus, for example, many personal computers and workstations are beingconfigured as multimedia devices that are capable of receiving imagedata, for example, directly from a digital camera or indirectly fromanother networked device. These so-called multimedia devices are furtherconfigured to display the image data (e.g., still images, video, etc.).As for still images and single video frames, most multimedia devices canbe further coupled to a printing device that is configured to provide aprinted hardcopy of the image data.

When provided with the appropriate software application(s), themultimedia device can be configured to allow the user to manipulate allor portions of the image data in some manner. For example, there is avariety of photo/drawing manipulation applications and video editingapplications available today. One example of a photo/drawingmanipulation program is PhotoDraw® 2000, available from the MicrosoftCorporation of Redmond, Wash. Another example of an image manipulationprogram is Picture It! 2000, also available from the MicrosoftCorporation. One example of a video editing application is AdobePremiere 6.0 available from Adobe Systems Incorporated of San Jose,Calif.

These and other image manipulation programs provide a multitude of imageediting tools/features. In some instances, for example, in the key-frameevaluation and photo quality estimation features of Picture It! 2000,the image manipulation program may need to calculate certaincharacteristics associated with the image data in terms of its'blurriness/sharpness. Doing so allows for the user and/or theapplication to selectively or automatically manipulate blurred imagedata in some desired fashion. For example, a blurred portion of theimage may be sharpened or perhaps protected from additional blurring.

With this in mind, previous methods for calculating blur characteristicshave been designed for image restoration. By way of example, see thearticle by M. C. Chiang and T. E. Boult, titled “Local Blur Estimationand Super-Resolution”, as published in Proc. IEEE Computer SocietyConference on Computer Vision and Pattern Recognition, pp. 821-826, June1997. Also, for example, see the article by R. L. Lagendijk, A. M.Tekalp and J. Biemond, titled “Maximum Likelihood Image and BlurIdentification: A Unifying Approach” as published in OpticalEngineering, 29(5):422-435, May 1990.

These exemplary conventional techniques utilize methods that estimatethe parameters needed by the reverse process of blur. Unfortunately,these methods tend to be complex and time-consuming.

Still other techniques utilize compressed domain methods based ondiscrete cosign transform (DCT) coefficient statistics, which can beused to estimate the blurriness of motion picture expert group (MPEG)frame in real-time. For example, see the methods presented by XavierMarichal, Wei-Ying Ma and HongJiang Zhang at the InternationalConference on Image Processing (ICIP) in Kobe, Japan on Oct. 25-29,1999, as published in an article titled “Blur Determination in theCompressed Domain Using DCT Information”. Unfortunately, these methodsoften find it difficult to handle images with relatively large uni-colorpatches.

Hence, there is an on-going need for new and improved methods forcalculating or otherwise determining blurriness/sharpnesscharacteristics in an image.

SUMMARY

The present invention provides new and improved methods and arrangementsfor calculating blurriness/sharpness characteristics in an image. Inaccordance with certain aspects of the present invention, the methodsand arrangements can be provided in a variety of devices or appliancesand used to support image rendering/presentation processes, imagemanipulation processes, and/or other like image data related processes.In accordance with certain exemplary implementations of the presentinvention, the improved methods and arrangements employ a multi-scaleedge amplitude comparison to evaluate the quality of an image, ratherthan estimating blurriness characteristics as in the conventionalmethods described above. Furthermore, in certain implementations, themulti-scale edge amplitude comparison is automatically adaptable to theimage content.

Thus, for example, in accordance with certain exemplary implementationsof the present invention, the above stated needs and others are met by amethod that includes detecting edges in a plurality of correspondingdifferent resolution images, and for each detected edge, comparingcorresponding edge parameters associated with the detected edges in theplurality of corresponding different resolution images and determiningif the detected edge is blurred. In certain implementations the edgeparameters associated with the detected edges include edge amplitudes.

The method may also include generating the plurality of correspondingdifferent resolution images from a base image, such that the resultingplurality of corresponding different resolution images includes the baseimage and at least one corresponding lower resolution image. In certainimplementations, for example, the plurality of corresponding differentresolution images includes the base image a second corresponding lowerresolution image, and a third corresponding lower resolution image thatis also lower in resolution than the second corresponding lowerresolution image.

In detecting the edges in the plurality of corresponding differentresolution images, the method may further include generating acorresponding plurality of detected edge maps. In such a case, the stepof comparing the corresponding edge parameters associated with thedetected edges may also include comparing corresponding edge amplitudesas provided in the plurality of detected edge maps to generate a resultmap.

The method may include the additional step of calculating a blurparameter based on this result map. For example, the blur parametermight include a blur percentage.

In still other implementations, the method may also include the step ofdetermining if the base image is blurred based on a comparison of theblur parameter with at least one blur parameter threshold.

In accordance with certain further implementations of the presentinvention, an apparatus is provided. The apparatus includes an edgedetector that is configured to detect edge transitions in a plurality ofcorresponding different resolution images, an edge parameter comparatorthat is configured to compare corresponding edge parameters as detectedby the edge detector, and a blur calculator that is configured todetermine at least one blur parameter based on comparison results asdetermined by the edge parameter comparator. The apparatus may alsoinclude an image generator that is configured to generate the pluralityof corresponding different resolution images based on a base image, andprovide the plurality of corresponding different resolution images tothe edge detector. In still further implementations, the apparatus mayinclude a blur detector that is configured to determine if a base imageis blurred based on a comparison of the at least one blur parameter withat least one blur parameter threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the various methods and arrangements ofthe present invention may be had by reference to the following detaileddescription when taken in conjunction with the accompanying drawingswherein:

FIG. 1 is a block diagram that depicts an exemplary device, in the formof a computer, which is suitable for use with certain implementations ofthe present invention.

FIGS. 2a-b are line graphs depicting a step edge and a smoothed stepedge, respectively, within exemplary images.

FIG. 3 is an illustrative representation of a multi-scale image pyramidhaving a plurality of different resolutions of the same image, inaccordance with certain aspects of the present invention.

FIG. 4 is a line diagram depicting exemplary corresponding multi-scaleedge amplitudes, in accordance with certain aspects of the presentinvention.

FIG. 5 is a block diagram associated with an exemplary blur detectorsystem architecture, in accordance with certain implementations of thepresent invention.

FIG. 6 is a block diagram associated with an exemplary blur detectoralgorithm for use in the blur detector system architecture of FIG. 5,for example, in accordance with certain further implementations of thepresent invention.

DETAILED DESCRIPTION

Turning to the drawings, wherein like reference numerals refer to likeelements, the invention is illustrated as being implemented in asuitable computing environment. Although not required, the inventionwill be described in the general context of computer-executableinstructions, such as program modules, being executed by a personalcomputer. Generally, program modules include routines, programs,objects, components, data structures, etc. that perform particular tasksor implement particular abstract data types. Moreover, those skilled inthe art will appreciate that the invention may be practiced with othercomputer system configurations, including hand-held devices,multi-processor systems, microprocessor based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

FIG. 1 illustrates an example of a suitable computing environment 120 onwhich the subsequently described methods and arrangements may beimplemented.

Exemplary computing environment 120 is only one example of a suitablecomputing environment and is not intended to suggest any limitation asto the scope of use or functionality of the improved methods andarrangements described herein. Neither should computing environment 120be interpreted as having any dependency or requirement relating to anyone or combination of components illustrated in computing environment120.

The improved methods and arrangements herein are operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well known computingsystems, environments, and/or configurations that may be suitableinclude, but are not limited to, personal computers, server computers,thin clients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

As shown in FIG. 1, computing environment 120 includes a general-purposecomputing device in the form of a computer 130. The components ofcomputer 130 may include one or more processors or processing units 132,a system memory 134, and a bus 136 that couples various systemcomponents including system memory 134 to processor 132.

Bus 136 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus also known as Mezzaninebus.

Computer 130 typically includes a variety of computer readable media.Such media may be any available media that is accessible by computer130, and it includes both volatile and non-volatile media, removable andnon-removable media.

In FIG. 1, system memory 134 includes computer readable media in theform of volatile memory, such as random access memory (RAM) 140, and/ornon-volatile memory, such as read only memory (ROM) 138. A basicinput/output system (BIOS) 142, containing the basic routines that helpto transfer information between elements within computer 130, such asduring start-up, is stored in ROM 138. RAM 140 typically contains dataand/or program modules that are immediately accessible to and/orpresently being operated on by processor 132.

Computer 130 may further include other removable/non-removable,volatile/non-volatile computer storage media. For example, FIG. 1illustrates a hard disk drive 144 for reading from and writing to anon-removable, non-volatile magnetic media (not shown and typicallycalled a “hard drive”), a magnetic disk drive 146 for reading from andwriting to a removable, non-volatile magnetic disk 148 (e.g., a “floppydisk”), and an optical disk drive 150 for reading from or writing to aremovable, non-volatile optical disk 152 such as a CD-ROM, CD-R, CD-RW,DVD-ROM, DVD-RAM or other optical media. Hard disk drive 144, magneticdisk drive 146 and optical disk drive 150 are each connected to bus 136by one or more interfaces 154.

The drives and associated computer-readable media provide nonvolatilestorage of computer readable instructions, data structures, programmodules, and other data for computer 130. Although the exemplaryenvironment described herein employs a hard disk, a removable magneticdisk 148 and a removable optical disk 152, it should be appreciated bythose skilled in the art that other types of computer readable mediawhich can store data that is accessible by a computer, such as magneticcassettes, flash memory cards, digital video disks, random accessmemories (RAMs), read only memories (ROM), and the like, may also beused in the exemplary operating environment.

A number of program modules may be stored on the hard disk, magneticdisk 148, optical disk 152, ROM 138, or RAM 140, including, e.g., anoperating system 158, one or more application programs 160, otherprogram modules 162, and program data 164.

The improved methods and arrangements described herein may beimplemented within operating system 158, one or more applicationprograms 160, other program modules 162, and/or program data 164.

A user may provide commands and information into computer 130 throughinput devices such as keyboard 166 and pointing device 168 (such as a“mouse”). Other input devices (not shown) may include a microphone,joystick, game pad, satellite dish, serial port, scanner, camera, etc.These and other input devices are connected to the processing unit 132through a user input interface 170 that is coupled to bus 136, but maybe connected by other interface and bus structures, such as a parallelport, game port, or a universal serial bus (USB).

A monitor 172 or other type of display device is also connected to bus136 via an interface, such as a video adapter 174. In addition tomonitor 172, personal computers typically include other peripheraloutput devices (not shown), such as speakers and printers, which may beconnected through output peripheral interface 175.

Computer 130 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer182. Remote computer 182 may include many or all of the elements andfeatures described herein relative to computer 130.

Logical connections shown in FIG. 1 are a local area network (LAN) 177and a general wide area network (WAN) 179. Such networking environmentsare commnonplace in offices, enterprise-wide computer networks,intranets, and the Internet.

When used in a LAN networking environment, computer 130 is connected toLAN 177 via network interface or adapter 186. When used in a WANnetworking environment, the computer typically includes a modem 178 orother means for establishing communications over WAN 179. Modem 178,which may be internal or external, may be connected to system bus 136via the user input interface 170 or other appropriate mechanism.

Depicted in FIG. 1, is a specific implementation of a WAN via theInternet. Here, computer 130 employs modem 178 to establishcommunications with at least one remote computer 182 via the Internet180.

In a networked environment, program modules depicted relative tocomputer 130, or portions thereof, may be stored in a remote memorystorage device. Thus, e.g., as depicted in FIG. 1, remote applicationprograms 189 may reside on a memory device of remote computer 182. Itwill be appreciated that the network connections shown and described areexemplary and other means of establishing a communications link betweenthe computers may be used.

This description will now focus on certain aspects of the presentinvention associated with image processing/handling.

Human vision often relies upon visible edge transitional information toevaluate the quality of an image. For example, when looking at an imageof a completely white painted smooth wall it would be difficult, if notimpossible, for a person to determine if the image or a portion thereofis blurred. However, if a black line has been drawn across the surfaceof the wall, a person would be more likely to determine if the image orat least the portion containing the black line is blurred. For example,if the entire image is blurred, than the black line will appear fuzzy,wider, and/or perhaps gray, etc., as would be expected for a blurredline/image.

Recognizing this human ability to detect the blurriness/sharpness of aline or color/pattern based on the edges, the exemplary methods andarrangements described herein provide a similar technique for devices.

With this in mind, attention is drawn to FIGS. 2a-b, which are linegraphs depicting a step edge and a smoothed step edge, respectively,within exemplary images. These line graphs depict the changingamplitudes of the image data at a certain points (e.g., pixels). Thestep edge, as represented by line 202 in FIG. 2a, illustrates that theamplitude of the image data changes abruptly between a first portion ofthe image (region 204) and a second portion of the image (region 206).This so-called step edge would tend to indicate that the image atregions 204 and 206 is more than likely not blurred, but instead issignificantly sharp.

To the contrary, the smoothed step edge, as represented by line 208 inFIG. 2b, illustrates that the amplitude of the image data changesgradually between a first portion of the image (region 210) and a secondportion of the image (region 212). This so-called smoothed step edgewould tend to indicate that the image at regions 210 and 212 is morethan likely blurred, since it is not as sharp a change as the step edgein FIG. 2a.

Reference is now made to FIG. 3, which is an illustrative representationof a multi-scale image pyramid 300 having a plurality of differentresolutions of the same image, in accordance with certain aspects of thepresent invention.

Multi-scale image pyramid 300, as will be described in greater detailbelow, provides a basis for determining if a detected edge within animage is sufficiently blurred enough to be considered blurred or if thedetected edge is sufficiently sharp enough to be considered sharp (ornot blurred).

In this example, multi-scale image pyramid 300, includes a base image302 (which may be part of a larger original image 301, for example)having a resolution of 100×100 pixels, a corresponding second image 304having a reduced resolution of 75×75 pixels, and a corresponding thirdimage 306 having an even more reduced resolution of 50×50 pixels. Here,second image 304 and third image 306 have each been generated from baseimage 302 using conventional resolution reduction techniques.

While exemplary multi-scale image pyramid 300 includes three levels ofresolution, those skilled in the art will recognize that the methods andarrangements described herein may be implemented with a greater orlesser number of multi-scaled images, as required.

With this in mind, based on multi-scale image pyramid 300, FIG. 4illustrates the amplitude of a smoothed step edge associated with twodifferent corresponding image resolutions, in accordance with certainaspects of the present invention.

Here, a differential operator is applied on the smoothed step edge. Asshown, the edge amplitude Δ will change according to the size σ of thedifferential operator. Let σ₁ and Δ₁ be associated with a lowerresolution image in multi-scale image pyramid 300, and σ₂ and Δ₂ beassociated with a higher resolution image in multi-scale image pyramid300. As shown, if σ₁>σ₂, then Δ₁>Δ₂. This property would not exist for asharp edge. Thus, a multi-scale edge amplitude comparison can be used todetect the blurriness/sharpness of images or portions thereof.

In accordance with certain aspects of the present invention, asdescribed in the exemplary methods and arrangements below, multi-scaledimages are used instead of multi-scale differential operators to reducethe computation complexity.

FIG. 5 presents a block diagram associated with an exemplary blurdetector system architecture, in accordance with certain implementationsof the present invention.

Here, an image handling mechanism 500 (e.g., an image rendering and/ormanipulation application, or like device/arrangement) includes a blurdetector 502 that is configured to receive or otherwise access baseimage 302 (which may be all or part of an original image) and todetermine if base image 302 is “blurred” or “not blurred” according tocertain selectively defined parameters.

FIG. 6 is a block diagram associated with an exemplary blur detectoralgorithm for use in blur detector 502 of FIG. 5, for example, inaccordance with certain further implementations of the presentinvention.

As depicted, blur detector 502 includes a series of functional blocksthat process base image 302 and determine if it is “blurred” or “notblurred”. First, base image 302 is provided to a multi-scale imagegenerator 602, which is configured to generate the images in multi-scaleimage pyramid 300 (FIG. 3). Next, the resulting multi-scale images areprovided to one or more edge operators or detectors, in this example,Sobel edge operators 604 a-b. The edge operators calculate an edgeamplitude on each of the pixels of an image. Pixels having an edgeamplitude greater than a preset threshold are called “edge pixels”. Theedge operators produce corresponding multi-scale edge maps 605, whichare then provided to a multi-scale edge amplitude comparator 606. Aresulting edge amplitude comparison map 607 is then provided to a blurpercentage calculator 608, which produces at least one blurrinessmeasurement, in this example, a blur percentage 609, which is thenprovided to threshold detector 610. Threshold detector 610 determines ifthe blurriness measurement(s) is within or without at least onethreshold range. For example, blur percentage 609 can be compared to adefined, selectively set threshold blur percentage.

In this manner a comparison of edge amplitudes for various resolutionsof base image 302 is made. For a given detected edge pixel of thirdimage 306, if the edge amplitude is greater than the corresponding edgeamplitude of second image 304, and if the edge amplitude of second image304 is greater than the corresponding edge amplitude of base image 302,then the detected edge pixel is mapped in result map 607 as “blurred”.This process is completed for all detected edge pixels of third image306. Blur percentage 609 of base image 302 can then be calculated bycomparing the number of pixels that are “blurred” in result map 607 withthe total number of edge pixels of third image 306. Thus, for example,in FIG. 3 if there are 1,000 edge pixels in third image 306, assuming700 of them have been mapped as “blurred”, then blur percentage 609would equal 70%. If the threshold percentage is set to 65%, thenthreshold detector 610 would consider base image 302 as being “blurred”.Conversely, if the threshold percentage is set to 75%, then thresholddetector 610 would consider base image 302 as being “not blurred”.

Moreover, by selectively controlling the size of base image 302, one canfurther determine if a portion of a larger image, as represented by baseimage 302, is blurred or not blurred. This may also be determined fromresult map 607. Hence, it may be useful to provide additional details asto which regions may or may not be determined to be blurred. Furtherimplementations may allow for additional threshold values, or ranges,that provide additional feedback to the user and/or image handlingmechanism 500.

Those skilled in the art will recognize that the above-describedexemplary methods and arrangements are adaptable for use with a varietyof color and monochrome image data, including, for example, RGB data,YUV data, CMYK data, etc.

Although some preferred embodiments of the various methods andarrangements of the present invention have been illustrated in theaccompanying Drawings and described in the foregoing DetailedDescription, it will be understood that the invention is not limited tothe exemplary embodiments disclosed, but is capable of numerousrearrangements, modifications and substitutions without departing fromthe spirit of the invention as set forth and defined by the followingclaims.

What is claimed is:
 1. A method comprising: detecting edges in aplurality of corresponding different resolution images; and for eachdetected edge, comparing corresponding edge parameters associated withthe detected edges in the plurality of corresponding differentresolution images and determining if the detected edge is blurred. 2.The method as recited in claim 1, wherein the corresponding edgeparameters associated with the detected edges in the plurality ofcorresponding different resolution images includes corresponding edgeamplitudes associated with the detected edges in the plurality ofcorresponding different resolution images.
 3. The method as recited inclaim 1, further comprising generating the plurality of correspondingdifferent resolution images from a base image.
 4. The method as recitedin claim 3, wherein the plurality of corresponding different resolutionimages includes the base image and at least one corresponding lowerresolution image.
 5. The method as recited in claim 3, wherein theplurality of corresponding different resolution images includes the baseimage a second corresponding lower resolution image, and a thirdcorresponding lower resolution image that is also lower in resolutionthan the second corresponding lower resolution image.
 6. The method asrecited in claim 3, wherein the base image is a portion of a largeroriginal image.
 7. The method as recited in claim 1, wherein detectingthe edges in the plurality of corresponding different resolution imagesfurther includes generating a corresponding plurality of detected edgemaps.
 8. The method as recited in claim 7, wherein comparing thecorresponding edge parameters associated with the detected edges in theplurality of corresponding different resolution images further includescomparing corresponding edge amplitudes as provided in the plurality ofdetected edge maps to generate a result map.
 9. The method as recited inclaim 8, further comprising calculating a blur parameter based on theresult map.
 10. The method as recited in claim 9, wherein the blurparameter includes a blur percentage parameter.
 11. The method asrecited in claim 9, further comprising generating the plurality ofcorresponding different resolution images from a base image anddetermining if the base image is blurred based on a comparison of theblur parameter with at least one blur parameter threshold.
 12. Acomputer-readable medium having computer-implementable instructions forperforming acts comprising: locating edges in a plurality ofcorresponding different resolution samples of an image; and for eachlocated edge, comparing corresponding edge parameters associated withthe located edges in the plurality of corresponding different resolutionsamples to determine if the located edge is significantly blurred. 13.The computer-readable medium as recited in claim 12, wherein thecorresponding edge parameters associated with the located edges in theplurality of corresponding different resolution samples includeamplitudes associated with located edges.
 14. The computer-readablemedium as recited in claim 12, having further computer-implementableinstructions for performing acts comprising: providing a base image filehaving image data associated with the image; and processing the baseimage to produce one or more corresponding different resolution samplesof the image.
 15. The computer-readable medium as recited in claim 14,wherein the plurality of corresponding different resolution samplesincludes the base image and at least one corresponding lower resolutionsample.
 16. The computer-readable medium as recited in claim 14, whereinthe plurality of corresponding different resolution samples includes thebase image, a second corresponding lower resolution sample, and a thirdcorresponding lower resolution sample that is also lower in resolutionthan the second corresponding lower resolution sample.
 17. Thecomputer-readable medium as recited in claim 14, wherein the base imageis a portion of a larger original image.
 18. The computer-readablemedium as recited in claim 12, wherein locating the edges in theplurality of corresponding different resolution samples further includesgenerating a corresponding plurality of detected edge maps.
 19. Thecomputer-readable medium as recited in claim 18, wherein comparing thecorresponding edge parameters associated with the located edges in theplurality of corresponding different resolution samples further includescomparing amplitudes as provided in the plurality of detected edge mapsto generate a result map.
 20. The computer-readable medium as recited inclaim 19, having further computer-implementable instructions forperforming acts comprising calculating at least one blur parameter basedon the result map.
 21. The computer-readable medium as recited in claim20, wherein the blur parameter includes a blur percentage.
 22. Thecomputer-readable medium as recited in claim 20, having furthercomputer-implementable instructions for performing acts comprising:creating the plurality of corresponding different resolution samplesfrom a base image; and determining if selected portions of the baseimage are blurred based on a comparison of the blur parameter with atleast one threshold value.
 23. An apparatus comprising: an edge detectorconfigured to detect edge transitions in a plurality of correspondingdifferent resolution images; an edge parameter comparator configured tocompare corresponding edge parameters as detected by the edge detector;and a blur calculator configured to determine at least one blurparameter based on comparison results as determined by the edgeparameter comparator.
 24. The apparatus as recited in claim 23, whereinthe corresponding edge parameters include corresponding edge amplitudesassociated with the detected edge transitions.
 25. The apparatus asrecited in claim 23, further comprising an image generator configured togenerate the plurality of corresponding different resolution imagesbased on an base image, and provide the plurality of correspondingdifferent resolution images to the edge detector.
 26. The apparatus asrecited in claim 25, wherein the plurality of corresponding differentresolution images includes the base image and at least one correspondinglower resolution image.
 27. The apparatus as recited in claim 25,wherein the plurality of corresponding different resolution imagesincludes the base image a second corresponding lower resolution image,and a third corresponding lower resolution image that is also lower inresolution than the second corresponding lower resolution image.
 28. Theapparatus as recited in claim 25, wherein the base image is a portion ofa larger original image.
 29. The apparatus as recited in claim 23,wherein the edge detector is further configured to generate acorresponding plurality of detected edge maps.
 30. The apparatus asrecited in claim 29, wherein the edge parameter comparator is furtherconfigured to compare corresponding edge amplitudes as provided in theplurality of detected edge maps to generate a result map.
 31. Theapparatus as recited in claim 30, wherein the blur calculator is furtherconfigured to calculate the at least one blur parameter based on theresult map.
 32. The apparatus as recited in claim 31, wherein the atleast one blur parameter includes a blur percentage parameter.
 33. Theapparatus as recited in claim 25, further comprising a blur detectorconfigured to determine if the base image is blurred based on acomparison of the at least one blur parameter with at least one blurparameter threshold.
 34. The apparatus as recited in claim 23, whereinthe apparatus is configured within logic and memory of a computer.